Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review

A variety of genome-wide profiling techniques are available to probe complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher-level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we provide a comparison among various modeling procedures for integrating genome-wide profiling data of gene copy number and transcriptional alterations and highlight common approaches to genomic data integration. A transparent benchmarking procedure is introduced to quantitatively compare the cancer gene prioritization performance of the alternative methods. The benchmarking algorithms and data sets are available at http://intcomp.r-forge.r-project.orgComment: PDF file including supplementary material. 9 pages. Preprin

canEvolve: A Web Portal for Integrative Oncogenomics

Background & objective: Genome-wide profiles of tumors obtained using functional genomics platforms are being deposited to the public repositories at an astronomical scale, as a result of focused efforts by individual laboratories and large projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium. Consequently, there is an urgent need for reliable tools that integrate and interpret these data in light of current knowledge and disseminate results to biomedical researchers in a user-friendly manner. We have built the canEvolve web portal to meet this need. Results: canEvolve query functionalities are designed to fulfill most frequent analysis needs of cancer researchers with a view to generate novel hypotheses. canEvolve stores gene, microRNA (miRNA) and protein expression profiles, copy number alterations for multiple cancer types, and protein-protein interaction information. canEvolve allows querying of results of primary analysis, integrative analysis and network analysis of oncogenomics data. The querying for primary analysis includes differential gene and miRNA expression as well as changes in gene copy number measured with SNP microarrays. canEvolve provides results of integrative analysis of gene expression profiles with copy number alterations and with miRNA profiles as well as generalized integrative analysis using gene set enrichment analysis. The network analysis capability includes storage and visualization of gene co-expression, inferred gene regulatory networks and protein-protein interaction information. Finally, canEvolve provides correlations between gene expression and clinical outcomes in terms of univariate survival analysis. Conclusion: At present canEvolve provides different types of information extracted from 90 cancer genomics studies comprising of more than 10,000 patients. The presence of multiple data types, novel integrative analysis for identifying regulators of oncogenesis, network analysis and ability to query gene lists/pathways are distinctive features of canEvolve. canEvolve will facilitate integrative and meta-analysis of oncogenomics datasets

The shaping and functional consequences of the dosage effect landscape in multiple myeloma

Background: Multiple myeloma (MM) is a malignant proliferation of plasma B cells. Based on recurrent aneuploidy such as copy number alterations (CNAs), myeloma is divided into two subtypes with different CNA patterns and patient survival outcomes. How aneuploidy events arise, and whether they contribute to cancer cell evolution are actively studied. The large amount of transcriptomic changes resultant of CNAs (dosage effect) pose big challenges for identifying functional consequences of CNAs in myeloma in terms of specific driver genes and pathways. In this study, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs. Results: We propose gene-wise dosage effect score and genome-wide karyotype plot as tools to measure and visualize concordant copy number and expression changes across cancer samples. We find that dosage effect in myeloma is widespread yet variable, and it is correlated with gene expression level and CNA frequencies in different chromosomes. Our analysis suggests that despite the enrichment of differentially expressed genes between hyperdiploid MM and non-hyperdiploid MM in the trisomy chromosomes, the chromosomal proportion of dosage sensitive genes is higher in the non-trisomy chromosomes. Dosage-sensitive genes are enriched by genes with protein translation and localization functions, and dosage resistant genes are enriched by apoptosis genes. These results point to future studies on differential dosage sensitivity and resistance of pro- and anti-proliferation pathways and their variation across patients as therapeutic targets and prognosis markers. Conclusions: Our findings support the hypothesis that recurrent CNAs in myeloma are selected by their functional consequences. The novel dosage effect score defined in this work will facilitate integration of copy number and expression data for identifying driver genes in cancer genomics studies. The accompanying R code is available at http://www.canevolve.org/dosageEffect/

Understanding the functional impact of copy number alterations in breast cancer using a network modeling approach

Copy number alterations (CNAs) are thought to account for 85% of the variation in gene expression observed among breast tumours. The expression of cis-associated genes is impacted by CNAs occurring at proximal loci of these genes, whereas the expression of trans-associated genes is impacted by CNAs occurring at distal loci. While a majority of these CNA-driven genes responsible for breast tumourigenesis are cis-associated, trans-associated genes are thought to further abet the development of cancer and influence disease outcomes in patients. Here we present a network-based approach that integrates copy-number and expression profiles to identify putative cis- and trans-associated genes in breast cancer pathogenesis. We validate these cis- and trans-associated genes by employing them to subtype a large cohort of breast tumours obtained from the METABRIC consortium, and demonstrate that these genes accurately reconstruct the ten subtypes of breast cancer. We observe that individual breast cancer subtypes are driven by distinct sets of cis- and trans-associated genes. Among the cis-associated genes, we recover several known drivers of breast cancer (e.g. CCND1, ERRB2, MDM2 and ZNF703) and some novel putative drivers (e.g. BRF2 and SF3B3). siRNA-mediated knockdown of BRF2 across a panel of breast cancer cell lines showed significant reduction specifically in cell proliferation in HER2+ lines, thereby indicating that BRF2 could be a context-dependent oncogene and potentially targetable in these lines. Among the trans-associated genes, we identify modules of immune-response (CD2, CD19, CD38 and CD79B), mitotic/cell-cycle kinases (e.g. AURKB, MELK, PLK1 and TTK), and DNA-damage response genes (e.g. RFC4 and FEN1).Comment: 23 pages, 2 tables, 7 figure

Sequencing Structural Variants in Cancer for Precision Therapeutics.

The identification of mutations that guide therapy selection for patients with cancer is now routine in many clinical centres. The majority of assays used for solid tumour profiling use DNA sequencing to interrogate somatic point mutations because they are relatively easy to identify and interpret. Many cancers, however, including high-grade serous ovarian, oesophageal, and small-cell lung cancer, are driven by somatic structural variants that are not measured by these assays. Therefore, there is currently an unmet need for clinical assays that can cheaply and rapidly profile structural variants in solid tumours. In this review we survey the landscape of 'actionable' structural variants in cancer and identify promising detection strategies based on massively-parallel sequencing.This work was supported by Cancer Research UK [grant numbers A15973, A15601: 454 G.M, J.D.B], VUmc Cancer Center Amsterdam [VUmc-CCA: BY] and the Dutch 455 Cancer Society [VU 2015-7882: BY].This is the author accepted manuscript. The final version is available from Cell/Elsevier via http://dx.doi.org/10.1016/j.tig.2016.07.00

Analysing multiple types of molecular profiles simultaneously: connecting the needles in the haystack

It has been shown that a random-effects framework can be used to test the association between a gene's expression level and the number of DNA copies of a set of genes. This gene-set modelling framework was later applied to find associations between mRNA expression and microRNA expression, by defining the gene sets using target prediction information. Here, we extend the model introduced by Menezes et al (2009) to consider the effect of not just copy number, but also of other molecular profiles such as methylation changes and loss-of-heterozigosity (LOH), on gene expression levels. We will consider again sets of measurements, to improve robustness of results and increase the power to find associations. Our approach can be used genome-wide to find associations, yields a test to help separate true associations from noise and can include confounders. We apply our method to colon and to breast cancer samples, for which genome-wide copy number, methylation and gene expression profiles are available. Our findings include interesting gene expression-regulating mechanisms, which may involve only one of copy number or methylation, or both for the same samples. We even are able to find effects due to different molecular mechanisms in different samples. Our method can equally well be applied to cases where other types of molecular (high-dimensional) data are collected, such as LOH, SNP genotype and microRNA expression data. Computationally efficient, it represents a flexible and powerful tool to study associations between high-dimensional datasets. The method is freely available via the SIM BioConductor package

Tracking Cancer Evolution through the Disease Course.

During cancer evolution, constituent tumor cells compete under dynamic selection pressures. Phenotypic variation can be observed as intratumor heterogeneity, which is propagated by genome instability leading to mutations, somatic copy-number alterations, and epigenomic changes. TRACERx was set up in 2014 to observe the relationship between intratumor heterogeneity and patient outcome. By integrating multiregion sequencing of primary tumors with longitudinal sampling of a prospectively recruited patient cohort, cancer evolution can be tracked from early- to late-stage disease and through therapy. Here we review some of the key features of the studies and look to the future of the field. SIGNIFICANCE: Cancers evolve and adapt to environmental challenges such as immune surveillance and treatment pressures. The TRACERx studies track cancer evolution in a clinical setting, through primary disease to recurrence. Through multiregion and longitudinal sampling, evolutionary processes have been detailed in the tumor and the immune microenvironment in non-small cell lung cancer and clear-cell renal cell carcinoma. TRACERx has revealed the potential therapeutic utility of targeting clonal neoantigens and ctDNA detection in the adjuvant setting as a minimal residual disease detection tool primed for translation into clinical trials

Identifying candidate drivers of drug response in heterogeneous cancer by mining high throughput genomics data

List of initial modulators for the resistant group. (TXT 1 kb

Intratumor heterogeneity and transcriptional profiling in glioblastoma: Translational opportunities

The study of phenotypic and genetic intratumor heterogeneity in glioblastoma is attracting a lot of attention. Recent studies have demonstrated that transcriptional profiling analysis can help interpret the complexity of this disease. Previously proposed molecular classifiers have been recently challenged due to the unexpected degree of intratumor heterogeneity that has been described spatially and at single-cell level. Different computational methods have been employed to analyze this huge amount of data, but new experimental designs including multisampling from individual patients and single-cell experiments require new specific approaches. In light of these results, there is hope that integration of genetic, phenotypic and transcriptional data coupled with functional experiments might help define new therapeutic strategies and classify patients according to key pathways and molecular targets that can be further investigated to develop personalized and combinatorial treatment strategies. This is the author accepted manuscript. The final version is available from Future Science Group via http://dx.doi.org/10.2217/fnl.15.1